Airbnb Data Exploration

library(ggplot2)
library(corrplot)
library(plotly)
library(gapminder)
library(dplyr)
library(randomForest)

# Importing the data 
nyc_path = "clean_datasets/NYC_CLEAN.csv"
boston_path_clean = "clean_datasets/BOSTON_CLEAN.csv"
boston_path = "clean_datasets/boston_data.csv"

nyc_dataset <- read.csv(nyc_path, header = TRUE)
nyc_dataset <- nyc_dataset[1:3000,]
glimpse(nyc_dataset)

Observations: 3,000
Variables: 16
$ X                              <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20...
$ id                             <int> 2539, 2595, 3647, 3831, 5022, 5099, 5121, 5178, 5203, 5238, 5295, 5441, ...
$ host_id                        <int> 2787, 2845, 4632, 4869, 7192, 7322, 7356, 8967, 7490, 7549, 7702, 7989, ...
$ neighbourhood_group            <fct> Brooklyn, Manhattan, Manhattan, Brooklyn, Manhattan, Manhattan, Brooklyn...
$ neighbourhood                  <fct> Kensington, Midtown, Harlem, Clinton Hill, East Harlem, Murray Hill, Bed...
$ latitude                       <dbl> 40.64749, 40.75362, 40.80902, 40.68514, 40.79851, 40.74767, 40.68688, 40...
$ longitude                      <dbl> -73.97237, -73.98377, -73.94190, -73.95976, -73.94399, -73.97500, -73.95...
$ room_type                      <fct> Private room, Entire home/apt, Private room, Entire home/apt, Entire hom...
$ price                          <int> 149, 225, 150, 89, 80, 200, 60, 79, 79, 150, 135, 85, 89, 85, 120, 140, ...
$ minimum_nights                 <int> 1, 1, 3, 1, 10, 3, 45, 2, 2, 1, 5, 2, 4, 2, 90, 2, 2, 1, 3, 7, 3, 2, 1, ...
$ number_of_reviews              <int> 9, 45, 0, 270, 9, 74, 49, 430, 118, 160, 53, 188, 167, 113, 27, 148, 198...
$ calculated_host_listings_count <int> 6, 2, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 3, 1, 1, 1, 1, 1, 1, 2, 1, 6, 6, 6, ...
$ availability_365               <int> 365, 355, 365, 194, 0, 129, 0, 220, 0, 188, 6, 39, 314, 333, 0, 46, 321,...
$ room_type_cat                  <int> 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, ...
$ neighbourhood_cat              <int> 108, 127, 94, 41, 61, 137, 13, 95, 202, 35, 202, 95, 182, 202, 209, 214,...
$ neighbourhood_group_cat        <int> 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, ...

boston_dataset <- read.csv(boston_path_clean, header = TRUE)
boston_dataset_numerical <- read.csv(boston_path, header = TRUE)

# Room type counts

all <- rbind(data.frame(fill = "NYC", room_types= nyc_dataset$room_type),
             data.frame(fill = "Boston", room_types= boston_dataset$room_type))

ggplot(nyc_dataset, aes(x = room_type,  fill = room_type)) + 
  geom_bar() + ggtitle('Room count NYC listing')

ggplot(boston_dataset, aes(x = room_type, fill = room_type)) + 
  geom_bar() + ggtitle('Room count Boston listing')

ggplot(all, aes(x = room_types, fill = fill)) + 
  geom_bar() + ggtitle('Room count Boston listing')

ggplot(nyc_dataset, aes(x=latitude, y=longitude, color=room_type)) +
  geom_point() + ggtitle('Map/room type NYC listing') +
  scale_color_brewer(palette = "Dark2")

ggplot(boston_dataset, aes(x=latitude, y=longitude, color=room_type)) +
  geom_point() + ggtitle('Map/room type Boston listing') +
  scale_color_brewer(palette = "Dark2")

ggplot(nyc_dataset, aes(x=neighbourhood_group, fill=room_type)) +
  geom_bar() + ggtitle("Neighbourhood Group with Room Type") +
  scale_color_brewer(palette = "Dark2")

# Price - NYC

nyc_dataset[which(nyc_dataset$price < 300),]

price_nyc <- nyc_dataset[nyc_dataset$price < 300,]

ggplot(price_nyc, aes(x=room_type, y=price)) +
  geom_boxplot() + ggtitle('Price per room type NYC')

ggplot(nyc_dataset, aes(x=latitude, y = longitude)) +
  geom_point() + ggtitle('Map of the NYC listings')

ggplot(price_nyc, aes(x=latitude, y=longitude, color=price)) +
  geom_point() + ggtitle('Map by price NYC listing')

# Number of reviews per price - NYC

ggplot(price_nyc, aes(x=number_of_reviews, y=price, color=neighbourhood_group)) +
  geom_point(alpha=.5) +
  scale_color_brewer(palette = 'Dark2')

min_night_vs_price <- subset(nyc_dataset, minimum_nights < 30 & price < 300)

ggplot(min_night_vs_price, aes(x=minimum_nights, y=price, color=room_type)) +
  geom_point(alpha=.1) +
  scale_color_brewer(palette = 'Dark2')

# Cleveland plot - NYC

ggplot(price_nyc, aes(x = price, y = neighbourhood_group)) +
  geom_segment(aes(yend = neighbourhood_group), xend = 0, colour = "grey50") +
  geom_point(size = 3, aes(colour = room_type)) +
  scale_colour_brewer(palette = "Set1", limits = c("Private room", "Entire home/apt", "Shared room")) +
  theme_bw() +
  theme(
    panel.grid.major.y = element_blank(),   
    legend.position = c(1, 0.55),           
    legend.justification = c(1, 0.5)
  )

ggplot(price_nyc, aes(x = number_of_reviews, y = neighbourhood_group)) +
  geom_segment(aes(yend = neighbourhood_group), xend = 0, colour = "grey50") +
  geom_point(size = 3, aes(colour = room_type)) +
  scale_colour_brewer(palette = "Set1", limits = c("Private room", "Entire home/apt", "Shared room")) +
  theme_bw() +
  theme(
    panel.grid.major.y = element_blank(),   
    legend.position = c(1, 0.55),           
    legend.justification = c(1, 0.5)
  )

ggplot(price_nyc, aes(x = reorder(neighbourhood_group, minimum_nights), y = minimum_nights)) +
  geom_point(size = 3) +  
  theme_bw() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.x = element_line(colour = "grey60", linetype = "dashed"),
    axis.text.x = element_text(angle = 60, hjust = 1)
  )

# Cleveland plot - BOSTON

ggplot(boston_dataset, aes(x = price, y = neighbourhood_cleansed)) +
  geom_segment(aes(yend = neighbourhood_cleansed), xend = 0, colour = "grey50") +
  geom_point(size = 3, aes(colour = room_type)) +
  scale_colour_brewer(palette = "Set1", limits = c("Private room", "Entire home/apt", "Shared room")) +
  theme_bw() +
  theme(
    panel.grid.major.y = element_blank(),   
    legend.position = c(1, 0.55),           
    legend.justification = c(1, 0.5)
  )

ggplot(boston_dataset, aes(x = number_of_reviews, y = neighbourhood_cleansed)) +
  geom_segment(aes(yend = neighbourhood_cleansed), xend = 0, colour = "grey50") +
  geom_point(size = 3, aes(colour = room_type)) +
  scale_colour_brewer(palette = "Set1", limits = c("Private room", "Entire home/apt", "Shared room")) +
  theme_bw() +
  theme(
    panel.grid.major.y = element_blank(),   
    legend.position = c(1, 0.55),           
    legend.justification = c(1, 0.5)
  )

ggplot(boston_dataset, aes(x = reorder(neighbourhood_cleansed, minimum_nights), y = minimum_nights)) +
  geom_point(size = 3) +  
  theme_bw() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.x = element_line(colour = "grey60", linetype = "dashed"),
    axis.text.x = element_text(angle = 60, hjust = 1)
  )

Below is shown an interactive plot similar to the last two:

fig <- ggplot(nyc_dataset ,aes(x=latitude, y=longitude, color=room_type)) +
  geom_point(alpha = 0.2) + ggtitle('Map/room type Boston listing - Interactive')
  scale_color_brewer(palette = "Dark2")

<ggproto object: Class ScaleDiscrete, Scale, gg>
    aesthetics: colour
    axis_order: function
    break_info: function
    break_positions: function
    breaks: waiver
    call: call
    clone: function
    dimension: function
    drop: TRUE
    expand: waiver
    get_breaks: function
    get_breaks_minor: function
    get_labels: function
    get_limits: function
    guide: legend
    is_discrete: function
    is_empty: function
    labels: waiver
    limits: NULL
    make_sec_title: function
    make_title: function
    map: function
    map_df: function
    n.breaks.cache: NULL
    na.translate: TRUE
    na.value: NA
    name: waiver
    palette: function
    palette.cache: NULL
    position: left
    range: <ggproto object: Class RangeDiscrete, Range, gg>
        range: NULL
        reset: function
        train: function
        super:  <ggproto object: Class RangeDiscrete, Range, gg>
    reset: function
    scale_name: brewer
    train: function
    train_df: function
    transform: function
    transform_df: function
    super:  <ggproto object: Class ScaleDiscrete, Scale, gg>

fig <- ggplotly(fig)
fig

fig <- ggplot(boston_dataset ,aes(x=latitude, y=longitude, color=room_type)) +
  geom_point(alpha = 0.2) + ggtitle('Map/room type Boston listing - Interactive')
  scale_color_brewer(palette = "Dark2")

<ggproto object: Class ScaleDiscrete, Scale, gg>
    aesthetics: colour
    axis_order: function
    break_info: function
    break_positions: function
    breaks: waiver
    call: call
    clone: function
    dimension: function
    drop: TRUE
    expand: waiver
    get_breaks: function
    get_breaks_minor: function
    get_labels: function
    get_limits: function
    guide: legend
    is_discrete: function
    is_empty: function
    labels: waiver
    limits: NULL
    make_sec_title: function
    make_title: function
    map: function
    map_df: function
    n.breaks.cache: NULL
    na.translate: TRUE
    na.value: NA
    name: waiver
    palette: function
    palette.cache: NULL
    position: left
    range: <ggproto object: Class RangeDiscrete, Range, gg>
        range: NULL
        reset: function
        train: function
        super:  <ggproto object: Class RangeDiscrete, Range, gg>
    reset: function
    scale_name: brewer
    train: function
    train_df: function
    transform: function
    transform_df: function
    super:  <ggproto object: Class ScaleDiscrete, Scale, gg>

fig <- ggplotly(fig)
fig

    
reviews_vs_price <- nyc_dataset[nyc_dataset$number_of_reviews < 100,]
reviews_vs_price <- nyc_dataset[nyc_dataset$price < 500,]

rev_vs_price <- ggplot(reviews_vs_price, aes(x=price, y=number_of_reviews, color=room_type)) +
    geom_point(alpha=.3)

ggplotly(rev_vs_price)

# Violin Plot
price_nyc <- nyc_dataset[which(nyc_dataset$price < 300),]

violin <- ggplot(price_nyc, aes(x=neighbourhood_group, y=price, fill=neighbourhood_group)) +
  geom_violin(trim=TRUE, adjust=0.8) +
  stat_summary(geom="point", fun="median") +
  ggtitle('Price per neighbourhood in NYC')

Ignoring unknown parameters: fun

ggplotly(violin)

No summary function supplied, defaulting to `mean_se()

Now let’s do some modeling. We are applying first Linear Regression on the NYC dataset in order to predict prices:

# Linear Regression Model
nyc_model = lm(price ~., data=nyc_dataset)

# The Model summary
summary(nyc_model)

Call:
lm(formula = price ~ ., data = nyc_dataset)

Residuals:
   Min     1Q Median     3Q    Max 
-298.9  -53.7  -18.0   18.6 4537.7 

Coefficients: (7 not defined because of singularities)
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            -8.104e+04  4.793e+04  -1.691 0.090984 .  
X                                       1.446e-02  2.670e-02   0.542 0.588125    
id                                     -5.057e-07  4.731e-05  -0.011 0.991472    
host_id                                -5.473e-06  1.917e-06  -2.855 0.004336 ** 
neighbourhood_groupBrooklyn             8.497e+01  1.650e+02   0.515 0.606501    
neighbourhood_groupManhattan            1.149e+02  1.308e+02   0.878 0.379771    
neighbourhood_groupQueens               4.393e+01  1.241e+02   0.354 0.723484    
neighbourhood_groupStaten Island        2.882e+01  3.401e+02   0.085 0.932474    
neighbourhoodArrochar                   7.960e+01  2.202e+02   0.362 0.717707    
neighbourhoodArverne                    1.523e+02  1.643e+02   0.927 0.354060    
neighbourhoodAstoria                   -1.457e+01  6.873e+01  -0.212 0.832148    
neighbourhoodBattery Park City          1.366e+02  1.392e+02   0.981 0.326491    
neighbourhoodBay Ridge                 -1.367e+02  1.039e+02  -1.315 0.188626    
neighbourhoodBayside                    2.539e+02  1.995e+02   1.273 0.203161    
neighbourhoodBedford-Stuyvesant        -2.447e+01  5.496e+01  -0.445 0.656146    
neighbourhoodBellerose                  2.118e+02  2.057e+02   1.030 0.303228    
neighbourhoodBensonhurst               -6.962e+01  1.352e+02  -0.515 0.606687    
neighbourhoodBoerum Hill                2.456e+01  7.362e+01   0.334 0.738692    
neighbourhoodBorough Park              -2.520e+01  1.335e+02  -0.189 0.850227    
neighbourhoodBriarwood                  7.555e+01  1.199e+02   0.630 0.528585    
neighbourhoodBrighton Beach            -2.396e+01  1.041e+02  -0.230 0.817942    
neighbourhoodBrooklyn Heights           1.536e+02  7.168e+01   2.143 0.032193 *  
neighbourhoodBushwick                  -9.934e+00  6.392e+01  -0.155 0.876501    
neighbourhoodCambria Heights            1.332e+02  1.691e+02   0.787 0.431139    
neighbourhoodCanarsie                   6.528e+01  9.584e+01   0.681 0.495827    
neighbourhoodCarroll Gardens            5.427e+01  6.145e+01   0.883 0.377216    
neighbourhoodChelsea                   -3.145e+01  2.717e+01  -1.158 0.247162    
neighbourhoodChinatown                 -6.543e+01  4.068e+01  -1.609 0.107824    
neighbourhoodCity Island                6.308e+01  1.965e+02   0.321 0.748206    
neighbourhoodCivic Center              -6.557e+00  1.040e+02  -0.063 0.949708    
neighbourhoodClason Point               6.306e+01  1.950e+02   0.323 0.746394    
neighbourhoodClifton                   -8.561e+01  2.694e+02  -0.318 0.750686    
neighbourhoodClinton Hill               4.675e+00  5.622e+01   0.083 0.933734    
neighbourhoodCobble Hill               -2.686e+01  7.842e+01  -0.343 0.731959    
neighbourhoodCollege Point              2.686e+02  1.902e+02   1.412 0.158059    
neighbourhoodColumbia St               -3.739e+01  1.144e+02  -0.327 0.743774    
neighbourhoodConcord                   -5.762e+01  2.341e+02  -0.246 0.805597    
neighbourhoodConcourse                  2.513e+01  1.334e+02   0.188 0.850570    
neighbourhoodConcourse Village          3.026e+00  1.953e+02   0.015 0.987639    
neighbourhoodCrown Heights             -2.548e+01  5.303e+01  -0.481 0.630846    
neighbourhoodCypress Hills              5.282e+01  9.757e+01   0.541 0.588291    
neighbourhoodDitmars Steinway           1.436e+01  8.013e+01   0.179 0.857779    
neighbourhoodDowntown Brooklyn         -3.035e+01  1.147e+02  -0.265 0.791357    
neighbourhoodDUMBO                      2.237e+01  9.662e+01   0.232 0.816899    
neighbourhoodEast Elmhurst              1.179e+01  1.193e+02   0.099 0.921276    
neighbourhoodEast Flatbush             -2.779e+01  6.786e+01  -0.410 0.682197    
neighbourhoodEast Harlem               -1.008e+02  5.560e+01  -1.814 0.069847 .  
neighbourhoodEast New York             -1.506e+01  9.486e+01  -0.159 0.873882    
neighbourhoodEast Village              -4.141e+01  2.572e+01  -1.610 0.107571    
neighbourhoodEastchester               -2.189e+01  1.933e+02  -0.113 0.909838    
neighbourhoodElmhurst                  -7.987e+00  1.010e+02  -0.079 0.937003    
neighbourhoodEmerson Hill              -8.543e+01  2.595e+02  -0.329 0.741997    
neighbourhoodFieldston                 -1.378e+02  1.943e+02  -0.709 0.478369    
neighbourhoodFinancial District        -7.624e+00  5.311e+01  -0.144 0.885870    
neighbourhoodFlatbush                  -2.964e+01  5.869e+01  -0.505 0.613628    
neighbourhoodFlatiron District          9.435e+01  5.907e+01   1.597 0.110304    
neighbourhoodFlatlands                 -1.171e+01  1.368e+02  -0.086 0.931814    
neighbourhoodFlushing                   8.571e+01  8.961e+01   0.957 0.338896    
neighbourhoodForest Hills               1.067e+01  1.896e+02   0.056 0.955121    
neighbourhoodFort Greene               -2.996e+01  5.665e+01  -0.529 0.596902    
neighbourhoodFort Hamilton             -1.128e+02  1.054e+02  -1.070 0.284920    
neighbourhoodGlendale                   1.138e+01  1.412e+02   0.081 0.935755    
neighbourhoodGowanus                   -1.286e+01  6.311e+01  -0.204 0.838525    
neighbourhoodGramercy                  -5.395e+01  4.554e+01  -1.185 0.236277    
neighbourhoodGraniteville              -1.695e+02  2.598e+02  -0.652 0.514188    
neighbourhoodGravesend                 -3.583e+01  1.366e+02  -0.262 0.793091    
neighbourhoodGreenpoint                 4.860e+00  6.930e+01   0.070 0.944092    
neighbourhoodGreenwich Village         -1.564e+01  3.477e+01  -0.450 0.652942    
neighbourhoodHarlem                    -1.025e+02  6.018e+01  -1.704 0.088547 .  
neighbourhoodHell's Kitchen            -7.366e+01  3.303e+01  -2.230 0.025805 *  
neighbourhoodHighbridge                -3.017e+01  1.518e+02  -0.199 0.842462    
neighbourhoodInwood                    -1.332e+02  9.812e+01  -1.357 0.174810    
neighbourhoodJackson Heights            1.237e+01  9.516e+01   0.130 0.896569    
neighbourhoodJamaica                    1.515e+02  1.352e+02   1.120 0.262690    
neighbourhoodKensington                -2.716e+01  6.324e+01  -0.430 0.667582    
neighbourhoodKew Gardens                5.995e+01  1.917e+02   0.313 0.754516    
neighbourhoodKingsbridge               -4.428e+01  1.303e+02  -0.340 0.733981    
neighbourhoodKips Bay                  -9.400e+01  4.272e+01  -2.200 0.027862 *  
neighbourhoodLittle Italy              -2.563e+01  6.967e+01  -0.368 0.713008    
neighbourhoodLong Island City           1.853e+01  7.342e+01   0.252 0.800734    
neighbourhoodLongwood                  -4.297e+00  1.949e+02  -0.022 0.982415    
neighbourhoodLower East Side           -5.267e+01  3.114e+01  -1.692 0.090813 .  
neighbourhoodMariners Harbor           -1.411e+02  2.613e+02  -0.540 0.589365    
neighbourhoodMaspeth                    3.176e+01  1.194e+02   0.266 0.790246    
neighbourhoodMiddle Village             6.672e+01  1.408e+02   0.474 0.635656    
neighbourhoodMidtown                   -5.725e+01  3.726e+01  -1.536 0.124586    
neighbourhoodMidwood                    2.281e+01  1.138e+02   0.201 0.841094    
neighbourhoodMorningside Heights       -1.229e+02  6.773e+01  -1.814 0.069739 .  
neighbourhoodMorris Heights            -2.309e+01  1.939e+02  -0.119 0.905239    
neighbourhoodMott Haven                -4.482e+00  1.520e+02  -0.029 0.976478    
neighbourhoodMount Eden                -1.980e+01  1.497e+02  -0.132 0.894799    
neighbourhoodMurray Hill               -1.704e+01  6.059e+01  -0.281 0.778545    
neighbourhoodNew Springville           -9.403e+01  2.561e+02  -0.367 0.713555    
neighbourhoodNoHo                       5.928e+00  7.437e+01   0.080 0.936466    
neighbourhoodNolita                     1.214e+02  3.906e+01   3.109 0.001895 ** 
neighbourhoodOzone Park                 4.945e+01  1.948e+02   0.254 0.799643    
neighbourhoodPark Slope                 1.058e+00  5.328e+01   0.020 0.984163    
neighbourhoodPort Morris                1.764e+01  1.359e+02   0.130 0.896683    
neighbourhoodPort Richmond              2.076e+01  2.327e+02   0.089 0.928922    
neighbourhoodProspect-Lefferts Gardens -3.167e+01  5.555e+01  -0.570 0.568674    
neighbourhoodProspect Heights           5.183e+01  5.483e+01   0.945 0.344620    
neighbourhoodQueens Village             1.240e+02  1.454e+02   0.853 0.393780    
neighbourhoodRed Hook                  -7.969e+01  1.142e+02  -0.698 0.485528    
neighbourhoodRego Park                  4.607e+01  1.217e+02   0.379 0.704990    
neighbourhoodRichmond Hill              4.525e+01  1.490e+02   0.304 0.761410    
neighbourhoodRidgewood                  2.429e+01  8.198e+01   0.296 0.766998    
neighbourhoodRockaway Beach             1.551e+02  2.183e+02   0.710 0.477558    
neighbourhoodRoosevelt Island          -7.461e+01  1.294e+02  -0.576 0.564385    
neighbourhoodSheepshead Bay            -6.444e+01  1.098e+02  -0.587 0.557250    
neighbourhoodShore Acres               -1.219e+02  2.393e+02  -0.509 0.610673    
neighbourhoodSoHo                      -2.784e+00  3.758e+01  -0.074 0.940941    
neighbourhoodSoundview                  2.552e+01  1.944e+02   0.131 0.895571    
neighbourhoodSouth Slope                7.910e+00  5.687e+01   0.139 0.889391    
neighbourhoodSpuyten Duyvil            -7.320e+01  1.946e+02  -0.376 0.706913    
neighbourhoodSt. Albans                 1.365e+02  1.225e+02   1.114 0.265214    
neighbourhoodSt. George                -6.132e+01  2.428e+02  -0.253 0.800637    
neighbourhoodStapleton                 -5.073e+01  2.699e+02  -0.188 0.850938    
neighbourhoodSunnyside                 -3.811e+00  7.533e+01  -0.051 0.959648    
neighbourhoodSunset Park               -8.019e+01  6.062e+01  -1.323 0.186021    
neighbourhoodTheater District          -2.693e+01  7.640e+01  -0.352 0.724494    
neighbourhoodTompkinsville             -8.481e+01  2.176e+02  -0.390 0.696693    
neighbourhoodTottenville                       NA         NA      NA       NA    
neighbourhoodTribeca                    1.023e+02  5.708e+01   1.792 0.073208 .  
neighbourhoodTwo Bridges               -7.174e+01  9.107e+01  -0.788 0.430880    
neighbourhoodUniversity Heights        -2.305e+01  1.934e+02  -0.119 0.905159    
neighbourhoodUpper East Side           -6.696e+01  4.245e+01  -1.577 0.114817    
neighbourhoodUpper West Side           -2.061e+01  4.230e+01  -0.487 0.626119    
neighbourhoodVinegar Hill              -3.753e+01  1.360e+02  -0.276 0.782648    
neighbourhoodWakefield                 -9.704e+00  1.936e+02  -0.050 0.960030    
neighbourhoodWashington Heights        -1.409e+02  7.976e+01  -1.767 0.077389 .  
neighbourhoodWest Village                      NA         NA      NA       NA    
neighbourhoodWilliamsbridge            -3.833e+00  1.931e+02  -0.020 0.984165    
neighbourhoodWilliamsburg              -8.518e+00  6.088e+01  -0.140 0.888736    
neighbourhoodWindsor Terrace                   NA         NA      NA       NA    
neighbourhoodWoodlawn                  -5.704e+01  1.935e+02  -0.295 0.768208    
neighbourhoodWoodside                          NA         NA      NA       NA    
latitude                                4.978e+02  6.244e+02   0.797 0.425443    
longitude                              -8.231e+02  5.186e+02  -1.587 0.112620    
room_typePrivate room                  -8.930e+01  7.089e+00 -12.597  < 2e-16 ***
room_typeShared room                   -9.954e+01  3.022e+01  -3.294 0.001001 ** 
minimum_nights                         -3.024e-01  1.179e-01  -2.564 0.010392 *  
number_of_reviews                      -1.494e-01  4.141e-02  -3.608 0.000314 ***
calculated_host_listings_count         -6.041e-01  6.950e-01  -0.869 0.384816    
availability_365                        1.513e-01  2.546e-02   5.944 3.12e-09 ***
room_type_cat                                  NA         NA      NA       NA    
neighbourhood_cat                              NA         NA      NA       NA    
neighbourhood_group_cat                        NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 175.4 on 2860 degrees of freedom
Multiple R-squared:  0.1589,    Adjusted R-squared:  0.1181 
F-statistic: 3.889 on 139 and 2860 DF,  p-value: < 2.2e-16

# Round the summary

coeffs <- summary(nyc_model)$coefficients
coeffs <- round(coeffs, 4)
coeffs

                                          Estimate Std. Error  t value Pr(>|t|)
(Intercept)                            -81037.7043 47928.5597  -1.6908   0.0910
X                                           0.0145     0.0267   0.5416   0.5881
id                                          0.0000     0.0000  -0.0107   0.9915
host_id                                     0.0000     0.0000  -2.8549   0.0043
neighbourhood_groupBrooklyn                84.9733   164.9546   0.5151   0.6065
neighbourhood_groupManhattan              114.8587   130.7509   0.8785   0.3798
neighbourhood_groupQueens                  43.9262   124.1411   0.3538   0.7235
neighbourhood_groupStaten Island           28.8169   340.0631   0.0847   0.9325
neighbourhoodArrochar                      79.5986   220.1528   0.3616   0.7177
neighbourhoodArverne                      152.3181   164.3320   0.9269   0.3541
neighbourhoodAstoria                      -14.5694    68.7342  -0.2120   0.8321
neighbourhoodBattery Park City            136.6015   139.1939   0.9814   0.3265
neighbourhoodBay Ridge                   -136.6811   103.9425  -1.3150   0.1886
neighbourhoodBayside                      253.9017   199.4691   1.2729   0.2032
neighbourhoodBedford-Stuyvesant           -24.4715    54.9566  -0.4453   0.6561
neighbourhoodBellerose                    211.8294   205.7155   1.0297   0.3032
neighbourhoodBensonhurst                  -69.6163   135.2126  -0.5149   0.6067
neighbourhoodBoerum Hill                   24.5605    73.6185   0.3336   0.7387
neighbourhoodBorough Park                 -25.2031   133.4584  -0.1888   0.8502
neighbourhoodBriarwood                     75.5490   119.8726   0.6302   0.5286
neighbourhoodBrighton Beach               -23.9557   104.0587  -0.2302   0.8179
neighbourhoodBrooklyn Heights             153.6113    71.6786   2.1431   0.0322
neighbourhoodBushwick                      -9.9344    63.9195  -0.1554   0.8765
neighbourhoodCambria Heights              133.1607   169.1241   0.7874   0.4311
neighbourhoodCanarsie                      65.2807    95.8379   0.6812   0.4958
neighbourhoodCarroll Gardens               54.2744    61.4537   0.8832   0.3772
neighbourhoodChelsea                      -31.4548    27.1746  -1.1575   0.2472
neighbourhoodChinatown                    -65.4296    40.6761  -1.6086   0.1078
neighbourhoodCity Island                   63.0786   196.4843   0.3210   0.7482
neighbourhoodCivic Center                  -6.5574   103.9551  -0.0631   0.9497
neighbourhoodClason Point                  63.0587   194.9694   0.3234   0.7464
neighbourhoodClifton                      -85.6126   269.4210  -0.3178   0.7507
neighbourhoodClinton Hill                   4.6748    56.2179   0.0832   0.9337
neighbourhoodCobble Hill                  -26.8643    78.4237  -0.3426   0.7320
neighbourhoodCollege Point                268.5508   190.1920   1.4120   0.1581
neighbourhoodColumbia St                  -37.3942   114.3934  -0.3269   0.7438
neighbourhoodConcord                      -57.6151   234.0811  -0.2461   0.8056
neighbourhoodConcourse                     25.1290   133.3753   0.1884   0.8506
neighbourhoodConcourse Village              3.0262   195.3064   0.0155   0.9876
neighbourhoodCrown Heights                -25.4834    53.0254  -0.4806   0.6308
neighbourhoodCypress Hills                 52.8208    97.5679   0.5414   0.5883
neighbourhoodDitmars Steinway              14.3613    80.1330   0.1792   0.8578
neighbourhoodDowntown Brooklyn            -30.3467   114.7004  -0.2646   0.7914
neighbourhoodDUMBO                         22.3724    96.6174   0.2316   0.8169
neighbourhoodEast Elmhurst                 11.7865   119.2530   0.0988   0.9213
neighbourhoodEast Flatbush                -27.7874    67.8555  -0.4095   0.6822
neighbourhoodEast Harlem                 -100.8311    55.5978  -1.8136   0.0698
neighbourhoodEast New York                -15.0590    94.8635  -0.1587   0.8739
neighbourhoodEast Village                 -41.4068    25.7231  -1.6097   0.1076
neighbourhoodEastchester                  -21.8909   193.2912  -0.1133   0.9098
neighbourhoodElmhurst                      -7.9868   101.0429  -0.0790   0.9370
neighbourhoodEmerson Hill                 -85.4251   259.4596  -0.3292   0.7420
neighbourhoodFieldston                   -137.7619   194.2986  -0.7090   0.4784
neighbourhoodFinancial District            -7.6240    53.1122  -0.1435   0.8859
neighbourhoodFlatbush                     -29.6354    58.6890  -0.5050   0.6136
neighbourhoodFlatiron District             94.3498    59.0674   1.5973   0.1103
neighbourhoodFlatlands                    -11.7083   136.8280  -0.0856   0.9318
neighbourhoodFlushing                      85.7110    89.6081   0.9565   0.3389
neighbourhoodForest Hills                  10.6730   189.6333   0.0563   0.9551
neighbourhoodFort Greene                  -29.9633    56.6500  -0.5289   0.5969
neighbourhoodFort Hamilton               -112.7787   105.4467  -1.0695   0.2849
neighbourhoodGlendale                      11.3825   141.1980   0.0806   0.9358
neighbourhoodGowanus                      -12.8613    63.1076  -0.2038   0.8385
neighbourhoodGramercy                     -53.9461    45.5399  -1.1846   0.2363
neighbourhoodGraniteville                -169.4701   259.7595  -0.6524   0.5142
neighbourhoodGravesend                    -35.8265   136.5737  -0.2623   0.7931
neighbourhoodGreenpoint                     4.8604    69.3016   0.0701   0.9441
neighbourhoodGreenwich Village            -15.6368    34.7696  -0.4497   0.6529
neighbourhoodHarlem                      -102.5286    60.1802  -1.7037   0.0885
neighbourhoodHell's Kitchen               -73.6608    33.0274  -2.2303   0.0258
neighbourhoodHighbridge                   -30.1726   151.8015  -0.1988   0.8425
neighbourhoodInwood                      -133.1676    98.1161  -1.3572   0.1748
neighbourhoodJackson Heights               12.3720    95.1634   0.1300   0.8966
neighbourhoodJamaica                      151.5045   135.2385   1.1203   0.2627
neighbourhoodKensington                   -27.1636    63.2428  -0.4295   0.6676
neighbourhoodKew Gardens                   59.9544   191.7194   0.3127   0.7545
neighbourhoodKingsbridge                  -44.2772   130.2777  -0.3399   0.7340
neighbourhoodKips Bay                     -94.0041    42.7223  -2.2003   0.0279
neighbourhoodLittle Italy                 -25.6284    69.6697  -0.3679   0.7130
neighbourhoodLong Island City              18.5320    73.4171   0.2524   0.8007
neighbourhoodLongwood                      -4.2969   194.9257  -0.0220   0.9824
neighbourhoodLower East Side              -52.6729    31.1361  -1.6917   0.0908
neighbourhoodMariners Harbor             -141.0688   261.3272  -0.5398   0.5894
neighbourhoodMaspeth                       31.7618   119.3978   0.2660   0.7902
neighbourhoodMiddle Village                66.7155   140.7999   0.4738   0.6357
neighbourhoodMidtown                      -57.2454    37.2629  -1.5363   0.1246
neighbourhoodMidwood                       22.8109   113.7631   0.2005   0.8411
neighbourhoodMorningside Heights         -122.8724    67.7250  -1.8143   0.0697
neighbourhoodMorris Heights               -23.0879   193.9242  -0.1191   0.9052
neighbourhoodMott Haven                    -4.4820   152.0012  -0.0295   0.9765
neighbourhoodMount Eden                   -19.7984   149.7097  -0.1322   0.8948
neighbourhoodMurray Hill                  -17.0410    60.5918  -0.2812   0.7785
neighbourhoodNew Springville              -94.0288   256.1239  -0.3671   0.7136
neighbourhoodNoHo                           5.9285    74.3662   0.0797   0.9365
neighbourhoodNolita                       121.4445    39.0600   3.1092   0.0019
neighbourhoodOzone Park                    49.4487   194.8088   0.2538   0.7996
neighbourhoodPark Slope                     1.0578    53.2826   0.0199   0.9842
neighbourhoodPort Morris                   17.6444   135.8691   0.1299   0.8967
neighbourhoodPort Richmond                 20.7590   232.7010   0.0892   0.9289
neighbourhoodProspect-Lefferts Gardens    -31.6668    55.5489  -0.5701   0.5687
neighbourhoodProspect Heights              51.8296    54.8326   0.9452   0.3446
neighbourhoodQueens Village               124.0316   145.4216   0.8529   0.3938
neighbourhoodRed Hook                     -79.6884   114.2436  -0.6975   0.4855
neighbourhoodRego Park                     46.0698   121.6747   0.3786   0.7050
neighbourhoodRichmond Hill                 45.2480   149.0096   0.3037   0.7614
neighbourhoodRidgewood                     24.2935    81.9808   0.2963   0.7670
neighbourhoodRockaway Beach               155.0815   218.3234   0.7103   0.4776
neighbourhoodRoosevelt Island             -74.6101   129.4397  -0.5764   0.5644
neighbourhoodSheepshead Bay               -64.4416   109.7814  -0.5870   0.5573
neighbourhoodShore Acres                 -121.8644   239.3398  -0.5092   0.6107
neighbourhoodSoHo                          -2.7842    37.5761  -0.0741   0.9409
neighbourhoodSoundview                     25.5161   194.3790   0.1313   0.8956
neighbourhoodSouth Slope                    7.9103    56.8732   0.1391   0.8894
neighbourhoodSpuyten Duyvil               -73.1967   194.6498  -0.3760   0.7069
neighbourhoodSt. Albans                   136.5205   122.5092   1.1144   0.2652
neighbourhoodSt. George                   -61.3215   242.8125  -0.2525   0.8006
neighbourhoodStapleton                    -50.7280   269.9175  -0.1879   0.8509
neighbourhoodSunnyside                     -3.8115    75.3252  -0.0506   0.9596
neighbourhoodSunset Park                  -80.1902    60.6234  -1.3228   0.1860
neighbourhoodTheater District             -26.9319    76.4037  -0.3525   0.7245
neighbourhoodTompkinsville                -84.8085   217.5542  -0.3898   0.6967
neighbourhoodTribeca                      102.2976    57.0796   1.7922   0.0732
neighbourhoodTwo Bridges                  -71.7430    91.0678  -0.7878   0.4309
neighbourhoodUniversity Heights           -23.0509   193.4491  -0.1192   0.9052
neighbourhoodUpper East Side              -66.9583    42.4488  -1.5774   0.1148
neighbourhoodUpper West Side              -20.6127    42.3042  -0.4872   0.6261
neighbourhoodVinegar Hill                 -37.5331   136.0409  -0.2759   0.7826
neighbourhoodWakefield                     -9.7036   193.6080  -0.0501   0.9600
neighbourhoodWashington Heights          -140.9051    79.7571  -1.7667   0.0774
neighbourhoodWilliamsbridge                -3.8327   193.0895  -0.0198   0.9842
neighbourhoodWilliamsburg                  -8.5181    60.8800  -0.1399   0.8887
neighbourhoodWoodlawn                     -57.0422   193.5292  -0.2947   0.7682
latitude                                  497.7551   624.4359   0.7971   0.4254
longitude                                -823.0831   518.6357  -1.5870   0.1126
room_typePrivate room                     -89.2965     7.0889 -12.5967   0.0000
room_typeShared room                      -99.5421    30.2219  -3.2937   0.0010
minimum_nights                             -0.3024     0.1179  -2.5642   0.0104
number_of_reviews                          -0.1494     0.0414  -3.6080   0.0003
calculated_host_listings_count             -0.6041     0.6950  -0.8692   0.3848
availability_365                            0.1513     0.0255   5.9436   0.0000

# Linear model for Boston

boston_model = lm(price ~., data=boston_dataset_numerical)

# The Model summary
summary(boston_model)

Call:
lm(formula = price ~ ., data = boston_dataset_numerical)

Residuals:
    Min      1Q  Median      3Q     Max 
-233.58  -23.51    0.91   21.47 1030.87 

Coefficients: (5 not defined because of singularities)
                                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     6.281e+03  2.100e+04   0.299 0.764948    
X                                               5.784e-03  8.765e-03   0.660 0.509365    
bathrooms                                       2.952e+01  3.463e+00   8.526  < 2e-16 ***
bedrooms                                        2.166e+01  2.731e+00   7.931 3.28e-15 ***
beds                                            6.802e+00  2.026e+00   3.357 0.000801 ***
cleaning_fee                                    1.739e-01  3.521e-02   4.938 8.44e-07 ***
availability_365                                1.682e-02  1.067e-02   1.578 0.114797    
number_of_reviews                              -2.366e-02  4.514e-02  -0.524 0.600149    
latitude                                       -2.618e+01  2.349e+02  -0.111 0.911266    
longitude                                       7.353e+01  2.230e+02   0.330 0.741601    
minimum_nights                                  5.115e-01  1.608e-01   3.180 0.001490 ** 
maximum_nights                                 -2.008e-07  6.668e-07  -0.301 0.763288    
property_type_Apartment                        -2.321e+01  6.647e+01  -0.349 0.726989    
property_type_Aprtament                         2.377e+01  9.386e+01   0.253 0.800098    
property_type_Bed...Breakfast                  -1.664e+01  6.803e+01  -0.245 0.806780    
property_type_Boat                             -3.819e+01  7.002e+01  -0.545 0.585504    
property_type_Camper.RV                        -2.317e+01  9.372e+01  -0.247 0.804788    
property_type_Condominium                      -2.350e+01  6.667e+01  -0.353 0.724478    
property_type_Dorm                             -1.728e+01  9.393e+01  -0.184 0.854091    
property_type_Entire.Floor                     -4.393e+01  8.175e+01  -0.537 0.591056    
property_type_Guesthouse                       -1.183e+01  9.496e+01  -0.125 0.900887    
property_type_House                            -2.205e+01  6.664e+01  -0.331 0.740804    
property_type_Loft                             -1.739e+01  6.746e+01  -0.258 0.796573    
property_type_Other                                    NA         NA      NA       NA    
property_type_Townhouse                         1.484e-01  6.747e+01   0.002 0.998245    
property_type_Villa                                    NA         NA      NA       NA    
room_type_Entire.home.apt                       2.396e+01  1.320e+01   1.815 0.069573 .  
room_type_Private.room                          9.178e+00  1.300e+01   0.706 0.480339    
room_type_Shared.room                                  NA         NA      NA       NA    
neighbourhood_cleansed_Allston                  3.977e+01  2.259e+01   1.761 0.078412 .  
neighbourhood_cleansed_Back.Bay                 8.511e+01  2.655e+01   3.205 0.001367 ** 
neighbourhood_cleansed_Bay.Village              9.707e+01  3.466e+01   2.801 0.005137 ** 
neighbourhood_cleansed_Beacon.Hill              9.064e+01  2.777e+01   3.264 0.001114 ** 
neighbourhood_cleansed_Brighton                 4.242e+01  2.124e+01   1.997 0.045925 *  
neighbourhood_cleansed_Charlestown              6.838e+01  3.088e+01   2.215 0.026882 *  
neighbourhood_cleansed_Chinatown                7.129e+01  3.286e+01   2.169 0.030155 *  
neighbourhood_cleansed_Dorchester               2.199e+01  2.517e+01   0.874 0.382396    
neighbourhood_cleansed_Downtown                 6.419e+01  2.799e+01   2.293 0.021929 *  
neighbourhood_cleansed_East.Boston              4.173e+01  3.525e+01   1.184 0.236562    
neighbourhood_cleansed_Fenway                   6.568e+01  2.237e+01   2.936 0.003354 ** 
neighbourhood_cleansed_Hyde.Park                3.388e+01  2.583e+01   1.312 0.189702    
neighbourhood_cleansed_Jamaica.Plain            5.327e+01  2.771e+01   1.922 0.054666 .  
neighbourhood_cleansed_Leather.District         1.089e+02  5.005e+01   2.177 0.029589 *  
neighbourhood_cleansed_Longwood.Medical.Area    3.845e+01  3.429e+01   1.121 0.262269    
neighbourhood_cleansed_Mattapan                 3.544e+01  3.077e+01   1.152 0.249513    
neighbourhood_cleansed_Mission.Hill             5.123e+01  2.820e+01   1.817 0.069395 .  
neighbourhood_cleansed_North.End                6.478e+01  3.396e+01   1.907 0.056596 .  
neighbourhood_cleansed_Roslindale               4.693e+01  2.936e+01   1.599 0.110045    
neighbourhood_cleansed_Roxbury                  5.699e+01  2.722e+01   2.093 0.036410 *  
neighbourhood_cleansed_South.Boston             4.298e+01  2.911e+01   1.477 0.139862    
neighbourhood_cleansed_South.Boston.Waterfront  7.980e+01  3.034e+01   2.631 0.008576 ** 
neighbourhood_cleansed_South.End                6.968e+01  2.783e+01   2.504 0.012362 *  
neighbourhood_cleansed_West.End                 5.177e+01  2.981e+01   1.736 0.082641 .  
neighbourhood_cleansed_West.Roxbury                    NA         NA      NA       NA    
bed_type_Airbed                                -1.387e+01  1.312e+01  -1.058 0.290318    
bed_type_Couch                                 -4.688e+01  3.139e+01  -1.494 0.135427    
bed_type_Futon                                 -1.317e+00  1.179e+01  -0.112 0.911093    
bed_type_Pull.out.Sofa                         -9.604e-01  1.701e+01  -0.056 0.954968    
bed_type_Real.Bed                                      NA         NA      NA       NA    
labels                                          6.127e+01  1.839e+00  33.322  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 66.08 on 2423 degrees of freedom
  (1107 observations deleted due to missingness)
Multiple R-squared:  0.702, Adjusted R-squared:  0.6953 
F-statistic: 105.7 on 54 and 2423 DF,  p-value: < 2.2e-16

# Round the summary - Boston

coeffs <- summary(boston_model)$coefficients
coeffs <- round(coeffs, 4)
coeffs

                                                Estimate Std. Error t value Pr(>|t|)
(Intercept)                                    6280.6920 21004.0248  0.2990   0.7649
X                                                 0.0058     0.0088  0.6599   0.5094
bathrooms                                        29.5212     3.4626  8.5256   0.0000
bedrooms                                         21.6584     2.7307  7.9314   0.0000
beds                                              6.8024     2.0264  3.3568   0.0008
cleaning_fee                                      0.1739     0.0352  4.9377   0.0000
availability_365                                  0.0168     0.0107  1.5776   0.1148
number_of_reviews                                -0.0237     0.0451 -0.5243   0.6001
latitude                                        -26.1775   234.8745 -0.1115   0.9113
longitude                                        73.5328   222.9811  0.3298   0.7416
minimum_nights                                    0.5115     0.1608  3.1803   0.0015
maximum_nights                                    0.0000     0.0000 -0.3012   0.7633
property_type_Apartment                         -23.2097    66.4705 -0.3492   0.7270
property_type_Aprtament                          23.7693    93.8576  0.2532   0.8001
property_type_Bed...Breakfast                   -16.6418    68.0345 -0.2446   0.8068
property_type_Boat                              -38.1902    70.0179 -0.5454   0.5855
property_type_Camper.RV                         -23.1667    93.7229 -0.2472   0.8048
property_type_Condominium                       -23.5031    66.6713 -0.3525   0.7245
property_type_Dorm                              -17.2750    93.9265 -0.1839   0.8541
property_type_Entire.Floor                      -43.9313    81.7512 -0.5374   0.5911
property_type_Guesthouse                        -11.8273    94.9573 -0.1246   0.9009
property_type_House                             -22.0476    66.6441 -0.3308   0.7408
property_type_Loft                              -17.3927    67.4626 -0.2578   0.7966
property_type_Townhouse                           0.1484    67.4696  0.0022   0.9982
room_type_Entire.home.apt                        23.9594    13.1972  1.8155   0.0696
room_type_Private.room                            9.1777    13.0021  0.7059   0.4803
neighbourhood_cleansed_Allston                   39.7739    22.5895  1.7607   0.0784
neighbourhood_cleansed_Back.Bay                  85.1120    26.5531  3.2053   0.0014
neighbourhood_cleansed_Bay.Village               97.0710    34.6577  2.8008   0.0051
neighbourhood_cleansed_Beacon.Hill               90.6365    27.7693  3.2639   0.0011
neighbourhood_cleansed_Brighton                  42.4234    21.2423  1.9971   0.0459
neighbourhood_cleansed_Charlestown               68.3762    30.8756  2.2146   0.0269
neighbourhood_cleansed_Chinatown                 71.2853    32.8606  2.1693   0.0302
neighbourhood_cleansed_Dorchester                21.9896    25.1698  0.8736   0.3824
neighbourhood_cleansed_Downtown                  64.1944    27.9950  2.2931   0.0219
neighbourhood_cleansed_East.Boston               41.7314    35.2487  1.1839   0.2366
neighbourhood_cleansed_Fenway                    65.6759    22.3674  2.9362   0.0034
neighbourhood_cleansed_Hyde.Park                 33.8784    25.8253  1.3118   0.1897
neighbourhood_cleansed_Jamaica.Plain             53.2684    27.7085  1.9225   0.0547
neighbourhood_cleansed_Leather.District         108.9491    50.0492  2.1768   0.0296
neighbourhood_cleansed_Longwood.Medical.Area     38.4462    34.2870  1.1213   0.2623
neighbourhood_cleansed_Mattapan                  35.4407    30.7696  1.1518   0.2495
neighbourhood_cleansed_Mission.Hill              51.2285    28.1995  1.8166   0.0694
neighbourhood_cleansed_North.End                 64.7807    33.9641  1.9073   0.0566
neighbourhood_cleansed_Roslindale                46.9329    29.3592  1.5986   0.1100
neighbourhood_cleansed_Roxbury                   56.9924    27.2237  2.0935   0.0364
neighbourhood_cleansed_South.Boston              42.9844    29.1066  1.4768   0.1399
neighbourhood_cleansed_South.Boston.Waterfront   79.8033    30.3357  2.6307   0.0086
neighbourhood_cleansed_South.End                 69.6776    27.8318  2.5035   0.0124
neighbourhood_cleansed_West.End                  51.7658    29.8142  1.7363   0.0826
bed_type_Airbed                                 -13.8729    13.1166 -1.0577   0.2903
bed_type_Couch                                  -46.8792    31.3881 -1.4935   0.1354
bed_type_Futon                                   -1.3165    11.7894 -0.1117   0.9111
bed_type_Pull.out.Sofa                           -0.9604    17.0064 -0.0565   0.9550
labels                                           61.2722     1.8388 33.3219   0.0000

# RANDOM FOREST - NYC
# Split the data into training and test

index = sample(2,nrow(nyc_dataset),replace = TRUE,prob=c(0.3,0.7))

# The training data

train_nyc = nyc_dataset[index==1,]

# The testing data

test_nyc = nyc_dataset[index==2,]

# The model

rfm = randomForest(price ~ latitude + longitude + neighbourhood_group_cat + minimum_nights + availability_365, data = train_nyc)

# Predictions

price_preds = predict(rfm, test_nyc[c("latitude", "longitude", "neighbourhood_group_cat", "minimum_nights", "availability_365")])

test_nyc$price_preds = price_preds

diff <- test_nyc[c("price", "price_preds")]
diff

ggplot(diff, aes(x=price, y=price_preds, color=)) + 
  geom_point(colour = "grey60") +
  stat_smooth(method = lm, level = 0.99, colour = "red")

# RANDOM FOREST - Boston
# Split the data into training and test

index = sample(2,nrow(boston_dataset_numerical),replace = TRUE,prob=c(0.3,0.7))

# The training data

train_boston = boston_dataset_numerical[index==1,]

# The testing data

test_boston = boston_dataset_numerical[index==2,]

# The model

train_boston <- na.omit(train_boston)
test_boston <- na.omit(test_boston)
rfm = randomForest(price ~ ., data = train_boston)

# Predictions

price_preds = predict(rfm, test_boston)

test_boston$price_preds = price_preds

diff <- test_boston[c("price", "price_preds")]
diff

ggplot(diff, aes(x=price, y=price_preds, color=)) + 
  geom_point(colour = "grey60") +
  stat_smooth(method = lm, level = 0.99, colour = "red")